Neutrino Monte-Carlo Event Generators and Cross-section Data

TL;DR

This paper reviews various neutrino interaction models implemented in Monte-Carlo generators, comparing their predictions to experimental data to evaluate their accuracy and consistency.

Contribution

It introduces a framework for tuning and comparing neutrino interaction models in Monte-Carlo generators against multiple experimental datasets.

Findings

Different models show varying degrees of agreement with data.

Some models better fit specific datasets than others.

The framework facilitates systematic comparison of models.

Abstract

In recent years a number of new theoretical models have been implemented into Monte-Carlo neutrino interaction event generators. Being able to compare multiple model predictions is invaluable to the field, especially as it is unfortunately still unclear which one provides the best fit to the entire collection of neutrino cross-section data. Using a recently developed neutrino generator tuning framework (NUISANCE), we review a selection of models in the NEUT and NuWro generators through comparisons to existing bubble chamber, MINERvA, and MiniBooNE cross-section data.